Efficient Simulation Sampling Allocation Using Multi-Fidelity Models

Simulation is often used to estimate the performance of alternative system designs for selecting the best. For a complex system, high-fidelity simulation is usually time-consuming and expensive. In this paper, we provide a new framework that integrates information from the multi-fidelity models to increase efficiency for selecting the best. A Gaussian mixture model is introduced to capture performance clustering information in the multi-fidelity models. Posterior information obtained by a clustering analysis incorporates both cluster-wise information and idiosyncratic information for each design. We propose a new budget allocation method to efficiently allocate high-fidelity simulation replications, utilizing posterior information. Numerical experiments show that the proposed multi-fidelity framework achieves a significant boost in efficiency.